Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 18, 4859-4884, 2018
https://doi.org/10.5194/acp-18-4859-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
10 Apr 2018
Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 1: Overview and model evaluation
Meng Gao1,2, Zhiwei Han3,4, Zirui Liu5, Meng Li6,7, Jinyuan Xin5, Zhining Tao8,9, Jiawei Li3,4, Jeong-Eon Kang10, Kan Huang11, Xinyi Dong11, Bingliang Zhuang12, Shu Li12, Baozhu Ge5, Qizhong Wu13, Yafang Cheng7, Yuesi Wang5, Hyo-Jung Lee10, Cheol-Hee Kim10, Joshua S. Fu11, Tijian Wang12, Mian Chin9, Jung-Hun Woo14, Qiang Zhang6, Zifa Wang4,5, and Gregory R. Carmichael1 1Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
2John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
3Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
4University of Chinese Academy of Sciences, Beijing 100049, China
5State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
6Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
7Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
8Universities Space Research Association, Columbia, MD, USA
9NASA Goddard Space Flight Center, Greenbelt, MD, USA
10Department of Atmospheric Sciences, Pusan National University, Busan, South Korea
11Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA
12School of Atmospheric Sciences, Nanjing University, Nanjing, China
13College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
14Department of Advanced Technology Fusion, Konkuk University, Seoul, South Korea
Abstract. Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-Asia III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry–meteorology models. Results are compared to meteorology and air quality measurements, including data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) and the Acid Deposition Monitoring Network in East Asia (EANET). The correlation coefficients between the multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5) for January 2010. However, large discrepancies exist between simulated aerosol chemical compositions from different models. The coefficient of variation (SD divided by the mean) can reach above 1.3 for sulfate in Beijing and above 1.6 for nitrate and organic aerosols in coastal regions, indicating that these compositions are less consistent from different models. During clean periods, simulated aerosol optical depths (AODs) from different models are similar, but peak values differ during severe haze events, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied relative humidity). These differences in composition and AOD suggest that future models can be improved by including new heterogeneous or aqueous pathways for sulfate and nitrate formation under hazy conditions, a secondary organic aerosol (SOA) formation chemical mechanism with new volatile organic compound (VOCs) precursors, yield data and approaches, and a more detailed evaluation of the dependence of aerosol optical properties on size distribution and mixing state. It was also found that using the ensemble mean of the models produced the best prediction skill. While this has been shown for other conditions (for example, the prediction of high-ozone events in the US (McKeen et al., 2005)), this is to our knowledge the first time it has been shown for heavy haze events.
Citation: Gao, M., Han, Z., Liu, Z., Li, M., Xin, J., Tao, Z., Li, J., Kang, J.-E., Huang, K., Dong, X., Zhuang, B., Li, S., Ge, B., Wu, Q., Cheng, Y., Wang, Y., Lee, H.-J., Kim, C.-H., Fu, J. S., Wang, T., Chin, M., Woo, J.-H., Zhang, Q., Wang, Z., and Carmichael, G. R.: Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 1: Overview and model evaluation, Atmos. Chem. Phys., 18, 4859-4884, https://doi.org/10.5194/acp-18-4859-2018, 2018.
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Short summary
Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-ASIA III Topic 3 study design is presented.
Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online...
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